def _apply(self, imgmsg, maskmsg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(imgmsg)
if img.ndim == 2:
img = gray2rgb(img)
mask = bridge.imgmsg_to_cv2(maskmsg, desired_encoding='mono8')
mask = mask.reshape(mask.shape[:2])
mask = gray2rgb(mask)
# compute label
roi = closed_mask_roi(mask)
roi_labels = masked_slic(img=img[roi],
mask=mask[roi],
n_segments=20,
compactness=30)
if roi_labels is None:
return
labels = np.zeros(mask.shape, dtype=np.int32)
# labels.fill(-1) # set bg_label
labels[roi] = roi_labels
if self.is_debugging:
# publish debug slic label
slic_labelmsg = bridge.cv2_to_imgmsg(labels)
slic_labelmsg.header = imgmsg.header
self.pub_slic.publish(slic_labelmsg)
# compute rag
g = rag_solidity(labels, connectivity=2)
if self.is_debugging:
# publish debug rag drawn image
rag_img = draw_rag(labels, g, img)
rag_img = img_as_uint(rag_img)
rag_imgmsg = bridge.cv2_to_imgmsg(rag_img.astype(np.uint8),
encoding='rgb8')
rag_imgmsg.header = imgmsg.header
self.pub_rag.publish(rag_imgmsg)
# merge rag with solidity
merged_labels = merge_hierarchical(labels,
g,
thresh=1,
rag_copy=False,
in_place_merge=True,
merge_func=_solidity_merge_func,
weight_func=_solidity_weight_func)
merged_labels += 1
merged_labels[mask == 0] = 0
merged_labelmsg = bridge.cv2_to_imgmsg(merged_labels.astype(np.int32))
merged_labelmsg.header = imgmsg.header
self.pub.publish(merged_labelmsg)
if self.is_debugging:
out = label2rgb(merged_labels, img)
out = (out * 255).astype(np.uint8)
out_msg = bridge.cv2_to_imgmsg(out, encoding='rgb8')
out_msg.header = imgmsg.header
self.pub_label.publish(out_msg)
def _apply(self, imgmsg, maskmsg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(imgmsg)
if img.ndim == 2:
img = gray2rgb(img)
mask = bridge.imgmsg_to_cv2(maskmsg, desired_encoding='mono8')
mask = mask.reshape(mask.shape[:2])
mask = gray2rgb(mask)
# compute label
roi = closed_mask_roi(mask)
roi_labels = masked_slic(img=img[roi], mask=mask[roi],
n_segments=20, compactness=30)
if roi_labels is None:
return
labels = np.zeros(mask.shape, dtype=np.int32)
# labels.fill(-1) # set bg_label
labels[roi] = roi_labels
if self.is_debugging:
# publish debug slic label
slic_labelmsg = bridge.cv2_to_imgmsg(labels)
slic_labelmsg.header = imgmsg.header
self.pub_slic.publish(slic_labelmsg)
# compute rag
g = rag_solidity(labels, connectivity=2)
if self.is_debugging:
# publish debug rag drawn image
rag_img = draw_rag(labels, g, img)
rag_img = img_as_uint(rag_img)
rag_imgmsg = bridge.cv2_to_imgmsg(
rag_img.astype(np.uint8), encoding='rgb8')
rag_imgmsg.header = imgmsg.header
self.pub_rag.publish(rag_imgmsg)
# merge rag with solidity
merged_labels = merge_hierarchical(
labels, g, thresh=1, rag_copy=False,
in_place_merge=True,
merge_func=_solidity_merge_func,
weight_func=_solidity_weight_func)
merged_labels += 1
merged_labels[mask == 0] = 0
merged_labelmsg = bridge.cv2_to_imgmsg(merged_labels.astype(np.int32))
merged_labelmsg.header = imgmsg.header
self.pub.publish(merged_labelmsg)
if self.is_debugging:
out = label2rgb(merged_labels, img)
out = (out * 255).astype(np.uint8)
out_msg = bridge.cv2_to_imgmsg(out, encoding='rgb8')
out_msg.header = imgmsg.header
self.pub_label.publish(out_msg)
def _apply(self, imgmsg, maskmsg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(imgmsg)
if img.ndim == 2:
img = gray2rgb(img)
mask = bridge.imgmsg_to_cv2(maskmsg, desired_encoding='mono8')
mask = mask.reshape(mask.shape[:2])
# compute label
roi = closed_mask_roi(mask)
roi_labels = masked_slic(img=img[roi],
mask=mask[roi],
n_segments=20,
compactness=30)
if roi_labels is None:
return
labels = np.zeros(mask.shape, dtype=np.int32)
# labels.fill(-1) # set bg_label
labels[roi] = roi_labels
if self.is_debugging:
# publish debug slic label
slic_labelmsg = bridge.cv2_to_imgmsg(labels)
slic_labelmsg.header = imgmsg.header
self.pub_slic.publish(slic_labelmsg)
# compute rag
g = rag_solidity(labels, connectivity=2)
if self.is_debugging:
# publish debug rag drawn image
if LooseVersion(skimage.__version__) >= '0.13.0':
fig, ax = plt.subplots(figsize=(img.shape[1] * 0.01,
img.shape[0] * 0.01))
show_rag(labels, g, img, ax=ax)
ax.axis('off')
plt.subplots_adjust(0, 0, 1, 1)
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
rag_img = np.fromstring(fig.canvas.tostring_rgb(),
dtype=np.uint8)
rag_img.shape = (h, w, 3)
plt.close()
else:
rag_img = draw_rag(labels, g, img)
rag_img = img_as_uint(rag_img)
rag_imgmsg = bridge.cv2_to_imgmsg(rag_img.astype(np.uint8),
encoding='rgb8')
rag_imgmsg.header = imgmsg.header
self.pub_rag.publish(rag_imgmsg)
# merge rag with solidity
merged_labels = merge_hierarchical(labels,
g,
thresh=1,
rag_copy=False,
in_place_merge=True,
merge_func=_solidity_merge_func,
weight_func=_solidity_weight_func)
merged_labels += 1
merged_labels[mask == 0] = 0
merged_labelmsg = bridge.cv2_to_imgmsg(merged_labels.astype(np.int32))
merged_labelmsg.header = imgmsg.header
self.pub.publish(merged_labelmsg)
if self.is_debugging:
out = label2rgb(merged_labels, img)
out = (out * 255).astype(np.uint8)
out_msg = bridge.cv2_to_imgmsg(out, encoding='rgb8')
out_msg.header = imgmsg.header
self.pub_label.publish(out_msg)
def main():
import argparse
import matplotlib
import matplotlib.pyplot as plt
import os
import time
from skimage.io import imread
from util import closed_mask_roi
from util import masked_slic
parser = argparse.ArgumentParser()
parser.add_argument('img_file')
parser.add_argument('depth_file')
args = parser.parse_args()
img_file = args.img_file
depth_file = args.depth_file
img = imread(img_file)
depth = imread(depth_file)
depth_rgb = gray2rgb(depth)
plt.figure(figsize=(24, 10))
plt.subplot(231)
plt.axis('off')
plt.imshow(depth_rgb)
t_start = time.time()
roi = closed_mask_roi(depth)
roi_labels = masked_slic(img=depth_rgb[roi], mask=depth[roi],
n_segments=20, compactness=30)
print('slic: takes {} [secs]'.format(time.time() - t_start))
labels = np.zeros_like(depth)
labels[roi] = roi_labels
print('n_labels: {}'.format(len(np.unique(labels))))
out = label2rgb(labels, img, bg_label=0)
plt.subplot(232)
plt.axis('off')
plt.imshow(out)
t_start = time.time()
g = rag_solidity(labels, connectivity=2)
print('rag_solidity: takes {} [secs]'.format(time.time() - t_start))
# draw rag: all
out = graph.draw_rag(labels, g, img)
plt.subplot(233)
plt.axis('off')
plt.imshow(out)
# draw rag: good edges
cmap = matplotlib.colors.ListedColormap(['#0000FF', '#FF0000'])
out = graph.draw_rag(labels, g, img, node_color='#00FF00', colormap=cmap,
thresh=1, desaturate=True)
plt.subplot(234)
plt.axis('off')
plt.imshow(out)
t_start = time.time()
merged_labels = graph.merge_hierarchical(
labels, g, thresh=1, rag_copy=False,
in_place_merge=True,
merge_func=_solidity_merge_func, weight_func=_solidity_weight_func)
print('graph.merge_hierarchical: takes {} [secs]'.format(time.time() - t_start))
out = label2rgb(merged_labels, img, bg_label=0)
plt.subplot(235)
plt.axis('off')
plt.imshow(out)
merged_g = rag_solidity(merged_labels)
# draw rag: all
out = graph.draw_rag(merged_labels, merged_g, img)
plt.subplot(236)
plt.axis('off')
plt.imshow(out)
# plt.show()
plt.savefig('{}_solidity_rag_merge.png'.format(time.time()), dpi=100)
======================================
This example constructs a Region Adjacency Graph (RAG) and draws it with
the `rag_draw` method.
"""
from skimage import data, segmentation
from skimage.future import graph
from skimage.util.colormap import viridis
from matplotlib import pyplot as plt, colors
img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
out = graph.draw_rag(labels, g, img)
plt.figure()
plt.title("RAG with all edges shown in green.")
plt.imshow(out)
# The color palette used was taken from
# http://www.colorcombos.com/color-schemes/2/ColorCombo2.html
cmap = colors.ListedColormap(['#6599FF', '#ff9900'])
out = graph.draw_rag(labels, g, img, node_color="#ffde00", colormap=cmap,
thresh=30, desaturate=True)
plt.figure()
plt.title("RAG with edge weights less than 30, color "
"mapped between blue and orange.")
plt.imshow(out)
plt.figure()
S = scene_segment()
while not rospy.is_shutdown():
if S.img == []: continue
print 'start'
img = S.img.copy()
labels = segmentation.slic(img, compactness=30, n_segments=200)
S.set_parameters(labels)
print labels
print '1 segment'
g = graph.rag_mean_color(img, labels)
#print g.nodes()
#print g.edges()
print '2 rag_mean_color'
out = graph.draw_rag(labels, g, img)
#seg = color.label2rgb(labels, img, kind='avg')
#print '5 label2rgb'
#print '6 mark_boundaries'
labels2 = {}
#for i in range(90,100,20):
i = 80
seg = segmentation.mark_boundaries(img, labels, (0, 0, 0))
cmap = colors.ListedColormap(['#6599FF', '#ff9900'])
out1 = graph.draw_rag(labels,
g,
seg,
node_color="#ffde00",
colormap=cmap,
Drawing Region Adjacency Graphs (RAGs)
======================================
This example constructs a Region Adjacency Graph (RAG) and draws it with
the `rag_draw` method.
"""
from skimage import data, segmentation
from skimage.future import graph
from skimage.util.colormap import viridis
from matplotlib import pyplot as plt, colors
img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
out = graph.draw_rag(labels, g, img)
plt.figure()
plt.title("RAG with all edges shown in green.")
plt.imshow(out)
# The color palette used was taken from
# http://www.colorcombos.com/color-schemes/2/ColorCombo2.html
cmap = colors.ListedColormap(['#6599FF', '#ff9900'])
out = graph.draw_rag(labels,
g,
img,
node_color="#ffde00",
colormap=cmap,
thresh=30,
desaturate=True)
plt.figure()
img = imread(img_file)
depth = imread(depth_file)
depth_rgb = gray2rgb(depth)
plt.figure(figsize=(24, 10))
plt.subplot(131)
plt.axis('off')
plt.imshow(depth_rgb)
roi = closed_mask_roi(depth)
roi_labels = masked_slic(img=depth_rgb[roi], mask=depth[roi],
n_segments=100, compactness=30)
labels = np.zeros_like(depth)
labels[roi] = roi_labels
out = label2rgb(labels, img, bg_label=0)
plt.subplot(132)
plt.axis('off')
plt.imshow(out)
g = rag_solidity(labels)
out = draw_rag(labels, g, img, colormap=viridis, desaturate=True)
plt.subplot(133)
plt.axis('off')
plt.imshow(out)
# plt.show()
plt.savefig('{}_rag_solidity.png'.format(time.time()))
"""
==========================
Region Boundary based RAGs
==========================
This example demonstrates construction of region boundary based RAGs with the
`rag_boundary` function.
"""
from skimage.future import graph
from skimage import data, segmentation, color, filters, io
from skimage.util.colormap import viridis
img = data.coffee()
gimg = color.rgb2gray(img)
labels = segmentation.slic(img, compactness=30, n_segments=400)
edges = filters.sobel(gimg)
edges_rgb = color.gray2rgb(edges)
g = graph.rag_boundary(labels, edges)
out = graph.draw_rag(labels, g, edges_rgb, node_color="#999999",
colormap=viridis)
io.imshow(out)
io.show()
"""
==========================
Region Boundary based RAGs
==========================
This example demonstrates construction of region boundary based RAGs with the
`rag_boundary` function.
"""
from skimage.future import graph
from skimage import data, segmentation, color, filters, io
from skimage.util.colormap import viridis
img = data.coffee()
gimg = color.rgb2gray(img)
labels = segmentation.slic(img, compactness=30, n_segments=400)
edges = filters.sobel(gimg)
edges_rgb = color.gray2rgb(edges)
g = graph.rag_boundary(labels, edges)
out = graph.draw_rag(labels,
g,
edges_rgb,
node_color="#999999",
colormap=viridis)
io.imshow(out)
io.show()
